CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \ --swap-space 8 \ --model "/tmp/model" --tensor-parallel-size 2 --port 30001 \ --gpu-memory-utilization 0.9 Pytorch 2.3.0 Temperature 0.6 FlashAttn enabled Prompt Hello, ChatGPT. From now on you are going to ...
# 需要导入模块: from torch import distributed [as 别名]# 或者: from torch.distributed importget_world_size[as 别名]def_gather(rank, rows, columns):dest =0tensor = _get_tensor(rank, rows, columns)ifrank == dest: tensors_list = _get_zeros_tensors_list(rows, columns) logger.debug('Ran...
in__init__ self.broadcast_bucket_size) File"/mnt/lustre/lirundong/Program/conda_env/torch-1.2-cuda-9.0/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 480,in_distributed_broadcast_coalesced dist._broadcast_coalesced(self.process_group, tensors, buffer_size) RuntimeError: ...
get_world_size() dist.all_reduce(coalesced, group=group_id) for grad, reduced in zip(grad_batch, _unflatten_tensors(coalesced, grad_batch)): grad.copy_(reduced) job_event.set() with torch.cuda.device(device_ids[0]): while True: _process_batch() # just to have a clear scope ...
# 需要导入模块: import Queue [as 别名]# 或者: from Queue importget[as 别名]defworker(sess,model_options,model_vars,Queue,CLASS_DICT):whileTrue:# print 'Queue Size', Queue.qsize()try: fname = Queue.get()except:returnstart = time.time() ...
换言之,在多GPU张量并行下,每张卡上 lm_head 的输出维度就不再是原来的 vocab_size 了,而是 vocab_size/#gpus。所以一种粗暴的解决办法就是把get_output_embeddings的输出改为 None 即可,如下: 代码语言:javascript 复制 defget_output_embeddings(self):returnNone # PretrainedModel.tie_weights 函数会将 lm_...
(input_text,return_tensors="pt")# Step 3: Pass the inputs through the modeloutputs=model(**inputs)# Step 4: Access the output logits or other desired outputslogits=outputs.logits# Step 5: Convert logits to probabilities or make predictionsprobabilities=logits.softmax(dim=-1)predictions=...
The new version supports mixed parallelism techniques from four-dimensional to five-dimensional, employing various parallel methods such as data parallelism, tensor model parallelism, pipeline parallelism, and grouped parameter slicing parallelism, effectively enhancing the training efficiency of large models....
Tensor Cores get smaller, this does not necessarily make GPU faster since the main problem for matrix multiplication is to get memory to the tensor cores which is dictated by SRAM and GPU RAM speed and size. GPU RAM still increases in speed if we stack memory modules into high-bandwidth ...
The new version supports mixed parallelism techniques from four-dimensional to five-dimensional, employing various parallel methods such as data parallelism, tensor model parallelism, pipeline parallelism, and grouped parameter slicing parallelism, effectively enhancing the training efficiency of large models....